class_docstr: Stores multiple instances of FairseqDataset together and in every iteration
  creates a batch by first sampling a dataset according to a specified
  probability distribution and then getting instances from that dataset.
  
  Args:
      datasets: an OrderedDict of FairseqDataset instances.
      sampling_func: A function for sampling over list of dataset keys.
          The default strategy is to sample uniformly.
class_docstr_tok: ["Stores","multiple","instances","of","FairseqDataset","together","and","in","every","iteration","creates","a","batch","by","first","sampling","a","dataset","according","to","a","specified","probability","distribution","and","then","getting","instances","from","that","dataset",".","Args",":","datasets",":","an","OrderedDict","of","FairseqDataset","instances",".","sampling_func",":","A","function","for","sampling","over","list","of","dataset","keys",".","The","default","strategy","is","to","sample","uniformly","."]
docstr: Length of this dataset is the sum of individual datasets
docstr_tok: ["Length","of","this","dataset","is","the","sum","of","individual","datasets"]
code: def __len__(self):
      """
      Length of this dataset is the sum of individual datasets
      """
      return self.total_num_instances
  
file_path: fairseq/fairseq/data/multi_corpus_sampled_dataset.py
imports: from collections import OrderedDict
  from typing import Callable, Dict, List
  import numpy
  from .None import FairseqDataset
label_desc: Use image node_id 2 for calling the MultiCorpusSampledDataset obj's underlying member method code with example usage: obj.__len__() and returns: self